Buch, Englisch, 496 Seiten
Buch, Englisch, 496 Seiten
ISBN: 978-1-394-38028-2
Verlag: John Wiley & Sons Inc
Harness artificial intelligence to develop stress-resilient crops for sustainable agriculture
Machine Learning and AI for Precision Plant Epigenetics demonstrates how to develop climate-resilient crops by integrating AI with RNA-based epigenetic technologies. Edited by Professor Jen-Tsung Chen, a leader in plant biotechnology, this volume integrates insightful contributions from experts around the world that discuss how ML and AI models can revolutionize plant breeding and crop improvement to ensure food security under changing environmental conditions.
The book explores applications across sixteen chapters, covering AI-driven epigenome engineering, CRISPR/Cas9-mediated precision editing, intelligent approaches to combat abiotic and biotic stresses, and AI-enabled RNA interference. It explores the use of AI models for studying non-coding RNAs, predicting plant epigenetic landscapes, unlocking heat stress memory mechanisms, and uncovering plant-microbiome interactions critical for productivity.
The book: - Integrates machine learning with RNA technologies to enhance epigenetic modifications through non-coding RNAs and refine gene silencing capabilities
- Demonstrates AI-advanced CRISPR/Cas systems for precision genome engineering to develop crops with enhanced quality, yield, and stress resilience
- Provides strategies for studying plant epigenetic landscapes under abiotic stress and developing intelligent priming systems against biotic threats
- Features contributions from leading international researchers at prestigious institutions
- Addresses ethical and regulatory considerations essential for responsible implementation of artificial intelligence in agricultural biotechnology and crop development
This essential resource is tailored for researchers in plant biology, stress physiology, crop breeding, computational biology, and bioinformatics. It offers a forward-looking perspective on developing sustainable agriculture systems that support global food security in an era of climate change and increasing environmental challenges.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
List of Contributors xix
About the Editor xxvii
Preface xxix
1 Machine Learning for Precision Epigenetic Modification in Plants 1
Elshan Musazade, Suxin Yang, and Xianzhong Feng
1.1 Introduction 1
1.2 Epigenetic 3
1.3 Epigenetic Modifications 4
1.3.1 DNA Methylation Dynamics 4
1.3.2 RNA Modification Dynamics 6
1.3.3 Histone Modifications and Chromatin Dynamics 7
1.3.4 Chromatin Remodeling and Nucleosome Dynamics 8
1.4 ml in Plant Epigenetics 9
1.4.1 ml in DNA Methylation Dynamics 13
1.4.2 ml in Histone Modifications 15
1.4.3 ml in Chromatin Remodeling 16
1.4.4 ml in Chromatin Modification Dynamics 17
1.4.5 ml in Chromatin- Interaction Prediction 18
1.4.6 ml in ncRNA Prediction 20
1.4.7 ml in Epitranscriptomics 21
1.4.8 ml in Epigenetic Genome Editing 22
1.5 Challenges and Limitations 23
1.6 Future Perspectives 25
1.7 Conclusion 26
References 27
2 AI- Driven Precision Plant Epigenetic Regulation Under Changing Climate 43
Mohd Anas, Neha Chaurasia, Yamshi Arif, Mohammad Danish, Nashra Waqar, and Mohammad Ali
2.1 Introduction 43
2.2 Foundations of Plant Epigenetics 44
2.2.1 DNA Methylation and Histone Modifications 44
2.2.2 Small RNAs and Noncoding RNA Regulation 45
2.2.3 Epigenetic Memory and Transgenerational Effects 46
2.3 Impacts of Climate Change on Plant Epigenomes 47
2.3.1 Abiotic Stress and Epigenomic Plasticity 47
2.3.2 Biotic Interactions in a Changing Climate 47
2.3.3 Case Studies 48
2.3.3.1 Epigenomic Responses to Salinity 48
2.3.3.2 Epigenomic Responses to Drought 49
2.3.3.3 Epigenomic Responses to Heat 50
2.4 Artificial Intelligence in Epigenetic Research 50
2.4.1 AI Tools and Algorithms for Omics Data Analysis 50
2.4.2 Machine Learning Models for Predicting Epigenetic Changes 51
2.4.3 Multiomics and Environmental Data Integrative Platforms 51
2.5 AI- Driven Precision Epigenetic Regulation 52
2.5.1 Epigenetic Biomarkers for Stress Tolerance 52
2.5.2 Targeted Epigenome Editing Strategy Design 52
2.5.3 AI- Enabled CRISPR/dCas Systems for Epigenetic Regulation 53
2.5.4 Reporting, Validation, and Ethics 53
2.5.4.1 Data and Code Transparency 53
2.5.4.2 Performance and Interpretation 53
2.5.4.3 Validation Ladder 54
2.5.4.4 Risk, Equity, and Access 55
2.6 Applications in Crop Improvement 55
2.6.1 Enhancing Resilience in Strategic Crops 55
2.6.1.1 Precision Breeding Through Genomic Tools 56
2.6.1.2 Systems Biology and Holistic Crop Management 56
2.6.1.3 Advanced Phenotyping for Precision Agriculture 57
2.6.2 Epigenetic Breeding and AI- Guided Selection 57
2.6.2.1 Mechanisms and Applications of Epigenetic Modulation 57
2.6.2.2 The Confluence of Epigenetics and Artificial Intelligence 58
2.6.2.3 Precision Epigenome Engineering 58
2.6.3 Data- Driven Decision- Making for Climate- Smart Agriculture 58
2.6.3.1 The Infrastructure of Precision Agriculture 59
2.6.3.2 Predictive Analytics for Proactive Management 59
2.6.3.3 Transparency and Efficiency Through Blockchain 60
2.7 Challenges, Ethics, and Future Directions 60
2.7.1 Data Limitations and Model Interpretability 60
2.7.2 Regulatory and Ethical Considerations 61
2.7.3 Prospects for Global Food Security 61
2.8 Conclusion 62
References 63
3 AI- Driven Plant Epigenome Engineering for Developing Resilient Crops 71
Neslihan Turgut Kara and Burcu Arikan
3.1 Introduction: Climate Change and the Need for Epigenetic Innovation 71
3.2 Epigenetic Mechanisms in Plant Stress Response 72
3.2.1 Epigenetic Memory in Environmental Stress Adaptation 74
3.3 Role of AI in Plant Epigenomic Decoding 75
3.3.1 AI- Driven Integration of Multiomics and Epigenetic Data 76
3.3.2 Deep Learning and Generative Network Approaches in Epigenetic Systems 77
3.3.3 Large Language Models in Plant Epigenomics 78
3.4 AI- Enabled Targeted Epigenome Editing 79
3.4.1 CRISPR/dCas9 Systems for Epigenetic Modifications: Designing Target Sites Through AI Prediction Models 80
3.5 Case Studies: Resilient Crop Development 81
3.5.1 Drought and Heat Tolerance Through Epigenetic Regulation 82
3.5.2 Salinity Resistance and Metabolic Adaptation 84
3.6 Conclusion and Future Perspectives 88
References 89
4 AI- Based Studies on Epigenetic Mechanisms: Highlighting Plant Adaptation and Domestication 99
Prioty Bandhan Roop and Sandip Debnath
4.1 Introduction 99
4.2 Epigenomic Data to Predictive Models: From Data to Discovery 101
4.3 AI/ML/DL Frameworks for Epigenetic Analysis in Plants 102
4.3.1 Machine Learning Approaches 102
4.3.2 Deep Learning for Epigenetic Pattern Recognition 103
4.3.2.1 CNNs for Spatial Epigenetic Patterns 103
4.3.2.2 RNNs for Temporal Epigenetic Dynamics 104
4.3.2.3 Graph Neural Networks for 3D Chromatin Architecture 105
4.3.2.4 Transformers for Multiomics Integration 107
4.3.2.5 Hybrid and Explainable AI Models 108
4.4 AI Insights into Epigenetic Mechanisms of Plant Adaptation 108
4.4.1 Drought Adaptation: Predictive Epigenomics and Transcriptomic Modeling 109
4.4.2 Salinity Stress: AI- Revealed Chromatin and Ion Homeostasis Networks 110
4.4.3 Heat Stress: AI- Modeled Epigenetic Dynamics and Thermotolerance 111
4.4.4 Cold Stress and Flooding: Temporal AI Modeling of Stress Memory 112
4.4.5 UV and Multistress Environments: Hybrid Adaptation Signatures 112
4.5 AI Insights into Plant Domestication Epigenomics 113
4.5.1 Epigenetic Footprints of Domestication in Crops 113
4.5.2 Cross- Species Generalization 115
4.6 Future Directions in AI- Driven Epigenomics 115
4.7 Conclusion 116
References 117
5 AI Models for Studying Plant Epigenetics and Epigenomics 127
Moahmed Kouighat, Abdelghani Bouchyoua, Anas Hamdani, and Yassine Mouniane
5.1 Introduction 127
5.2 Plant Epigenetics and Epigenomics: An Overview 128
5.2.1 Major Epigenetic Mechanisms in Plants 128
5.2.2 Major Epigenomic Mechanisms in Plants 129
5.3 AI Models for Plant Epigenetics and Epigenomics 130
5.3.1 Why AI for Epigenomics? 130
5.3.2 AI Models for Plant Epigenetics and Epigenomics 131
5.4 Application of AI Models in Plant Epigenetics 131
5.5 Challenges and Limitations 136
5.6 Future Directions 136
5.7 Conclusion 137
References 137
6 AI- Based Approaches for Studying Plant Epigenetic Landscapes Under Abiotic Stress 143
Sangeeta Sarma, Arpita Talukdar, Abdul Jalil, Bhanu Priya Pegu, Nazneen Hussain, Abhik Gogoi, Dhanawantari L. Singha, and Manabendra Dutta Choudhury
6.1 Introduction 143
6.1.1 Plant Epigenetic Mechanisms 144
6.1.2 Epigenetic Regulation of Plant Responses to Abiotic Stress 145
6.1.3 Challenges in Handling and Integrating Large- Scale Epigenetic Datasets 146
6.2 AI and Machine Learning in Epigenetics 146
6.2.1 Types of AI Techniques 147
6.2.2 Data Preprocessing, Feature Extraction, Model Training, and Evaluation 148
6.3 Application of AI in Studying Plant Epigenetic Responses to Abiotic Stress 149
6.3.1 Pattern Recognition and Classification 149
6.3.2 Predictive Modeling 150
6.3.3 Multiomics Data Integration 151
6.4 Limitations and Drawbacks 153
6.5 Conclusion 154
References 154
7 AI- Based Whole- Genome Prediction for Diverse Plant Epigenetic Modulations 161
Manoj Mani, Nahidha Parveen Mohammed Koya, Vivek Patel, Vivek Kumar Varshney, Antony Prabhu Jeyabal Philomenathan, Maria Jerline Babu, Akilandeswari Govindraj, and Vijaya Anand Arumugam
7.1 Introduction 161
7.2 Plant Epigenetic Modifications 163
7.2.1 DNA Methylation and Predictive Epigenomics 163
7.2.2 Histone Modifications and Chromatin Remodeling 163
7.2.3 Noncoding RNAs and Epigenetic Regulation 164
7.2.4 Transgenerational Epigenetic Inheritance 164
7.3 Data Sources and Profiling Technologies 165
7.3.1 Whole- Genome Bisulfite Sequencing 165
7.3.2 Chromatin Accessibility and Protein– DNA Interaction Profiling 165
7.3.3 RNA- Based Epigenomic Profiling 166
7.3.4 Single- Cell Epigenomics 166
7.3.5 Long- Read Sequencing and Direct Epigenetic Detection 166
7.3.6 Time Series and Multiomics Integration 167
7.4 AI and Machine Learning Frameworks for Epigenomic Prediction 167
7.4.1 Deep Learning Architectures for Epigenomic Inference 168
7.4.2 Transformer Models and Context- Aware Genomic Learning 168
7.4.3 Graph Neural Networks for 3D Genome Modeling 168
7.4.4 Generative Adversarial Networks for Synthetic Epigenomic Simulation 169
7.4.5 Federated Learning for Secure and Decentralized Epigenomic Prediction 169
7.5 Multiomics Integration Using AI 170
7.5.1 Systems Biology Approaches for Phenotype– Epigenome Prediction 170
7.5.2 Knowledge Graphs and Epigenetic Trait Modeling 171
7.6 AI- Based Whole- Genome Prediction Models 171
7.7 Applications in Plant Science and Agriculture 173
7.8 AI- Guided Epigenome Editing and Synthetic Biology 175
7.9 Explainable and Interpretable AI 176
7.10 Digital Twins and In Silico Plant Epigenomics 177
7.11 Quantum Computing and AI for Epigenetic Predictions 180
7.11.1 Hybrid Quantum- Classical AI Models in Epigenomic Prediction 180
7.11.2 Quantum- Enhanced Predictive Epigenomics in Agriculture 181
7.12 Challenges and Limitations 181
7.12.1 Data Sparsity, Noise, and Analytical Complexity 181
7.12.2 Computational and Biological Challenges in Genome- Wide Prediction 182
7.12.3 Cross- Species Transferability and Model Generalization 182
7.12.4 Ethical, Regulatory, and Biosafety Constraints 183
7.13 Future Perspectives 183
7.14 Conclusion 185
Abbreviations 185
Acknowledgement 186
Data Availability 186
References 187
8 Intelligent Priming System for Combating Biotic Stress 201
Fatima- Ezzahra Soussani, Fatima- Zahra Akensous, Abdelhamid Aouabe, Naira Sbbar, Rachid Lahlali, and Abdelilah Meddich
8.1 Introduction 201
8.2 Mechanisms Underlying Priming- Based Immunity 202
8.2.1 Signal Perception and Transduction 202
8.2.2 Role of Phytohormones 202
8.2.3 ROS Generation and Redox Signaling 203
8.3 Epigenetic and Molecular Basis of Priming Memory 203
8.3.1 DNA Methylation 203
8.3.2 Histone Modification 204
8.3.3 Transcriptional Reprogramming 204
8.3.4 Systemic Signal Transmission 205
8.4 Priming Agents and Triggers 206
8.4.1 Biotic: PGPR, Mycorrhizae, and Trichoderma spp. 206
8.4.2 Abiotic: Temperature Shifts, UV Light, and Chemical Inducers (e.g., BABA and SA) 206
8.4.3 Nanomaterials and Engineered Inducers 207
8.5 Integration with Omics and Smart Technologies 207
8.5.1 Transcriptomics, Proteomics, and Metabolomics in IPS Monitoring 207
8.5.2 Biosensors and Precision Agriculture Platforms in IPS Modulating 208
8.5.3 AI and Machine Learning for Predictive IPS Modeling 209
8.6 Applications in Major Crops 209
8.6.1 Case Studies in Tomato, Rice, Wheat, and Arabidopsis 209
8.6.2 IPS Deployment Under Field and Greenhouse Conditions 211
8.7 Transgenerational Priming and Long- Term Immunity 212
8.8 Advantages, Limitations, and Risk Assessment 213
8.9 Future Directions and Prospects 214
8.10 Conclusion 215
Acknowledgments 216
References 216
9 AI Technology for Studying Plant Noncoding RNAs 229
Amarjeet Singh Bhogal, Rinee Doley, Shibani Ritusmita Borah, Niharika Saharia, Olympica Das, Rekha Sharma, Satwik Subhankar, Shyamalin Rajmedhi, Dikshita Hazarika, and Debojit Sarma
9.1 Introduction 229
9.2 Overview of Plant Noncoding RNAs 230
9.2.1 Classification of ncRNA 230
9.2.1.1 MicroRNA 231
9.2.1.2 Si- RNAs 231
9.2.1.3 Long Noncoding RNA 231
9.2.2 Functional Roles and Mechanisms in Plant Growth, Development, Stress Responses, and Adaptation 232
9.2.2.1 Role in Plant Growth and Development 232
9.2.2.2 Role in Stress Response and Adaptation 233
9.2.3 Challenges in Studying Plant ncRNA 235
9.3 Traditional Approaches for ncRNA Analysis in Plants 236
9.3.1 Laboratory- Based Detection: RNA- seq, qRT- PCR, Northern Blot, In Situ Hybridization 236
9.3.1.1 Northern Blotting for RNA Detection 236
9.3.1.2 RT- PCR and qRT- PCR for Plant ncRNA Expression Validation 236
9.3.1.3 In Situ Hybridization 237
9.3.2 Computational Biology Before AI: Sequence Alignment, Comparative Genomics, and Motif Analysis 237
9.3.2.1 Cloning and Sanger Sequencing of ncRNA Genes 237
9.3.2.2 Comparative Genomics 238
9.3.2.3 Motif Analysis 238
9.4 Rise of AI in Plant ncRNA Research 238
9.5 AI- Based Tools and Pipelines for Plant ncRNA Analysis 240
9.5.1 Pinc 240
9.5.2 PlantLncBoost 241
9.5.3 LncFinder- Plant and CPATplant 242
9.5.4 LncADeep, RNAplonc, and DeepPInc 242
9.5.5 Plant- LncPipe 243
9.6 Applications of AI in ncRNA Research 244
9.6.1 AI for Identification and Classification of Novel Plant ncRNAs 245
9.6.1.1 Machine Learning Tools 245
9.6.1.2 Deep Learning Architectures 246
9.6.1.3 Plant- Specific Databases and High- Throughput Annotation 246
9.6.2 Functional and Regulatory Role Prediction 246
9.6.3 Network Reconstruction and Discovery of Regulatory Modules 247
9.6.4 Integration of AI with Omics and Big Data Platforms 248
9.7 Major Obstacles and Constraints in AI- Driven ncRNA Research in Flora 250
9.7.1 Limitations of the Dataset and Quality of Curation 250
9.7.2 Cross- Species Generalization in Plants 251
9.7.3 Barriers to Biological Validation 251
9.7.4 Interpretability and Explainability of AI Models 252
9.7.5 Integration with Multiomics Data 252
9.7.6 Resource and Accessibility Constraints 253
9.7.7 Ethical and Biosafety Considerations 253
9.8 Future Perspectives of AI Technology for Studying Plant ncRNAs 253
9.8.1 More Advanced Multiclass and Functional Predictive Frameworks 254
9.8.2 Integration with Multiomics 254
9.8.3 User- Friendly Tools and Better Access for Plant Breeders and Molecular Biologists 255
9.9 Conclusion 256
References 256
10 AI- Enabled Plant RNA Interference 265
Manoj Mani, Gnana Sowndariyan Gnanasekaran, Poovarasan Manjeeswaran, Kathiresan Nagaraj, Antony Prabhu Jeyabal Philomenathan, Akilandeswari Govindraj, Maria Jerline Babu, and Vijaya Anand Arumugam
10.1 Introduction to Plant RNA Interference 265
10.2 Computational Biology Foundations of RNAi 266
10.3 Artificial Intelligence in RNAi Research 268
10.4 AI- Driven RNAi Design and Optimization 270
10.5 Functional Genomics Through AI- Enhanced RNAi 272
10.6 AI and RNAi in Plant Stress and Disease Management 273
10.7 Integration of Multiomics Data with AI and RNAi 275
10.8 AI- Enhanced Delivery Systems for RNAi in Plants 277
10.9 Challenges, Limitations, and Ethical Considerations 280
10.10 Future Prospects and Next- Generation Directions 282
10.11 Conclusion 283
Abbreviations 284
Acknowledgement 285
Data Availability 285
References 285
11 AI Models for Uncovering Plant– Microbiome Interactions 299
Mohammed Radi, Hakima Achetoui, Ilham Dehbi, Hajar Zennouhi, and Rachid Lahlali
11.1 Introduction 299
11.2 Plant Microbiomes and Their Roles 301
11.2.1 Niches: Rhizosphere, Phyllosphere, and Endosphere 301
11.2.1.1 Function Activity of Microbial Consortia in the Rhizosphere 301
11.2.1.2 Rhizospheric Region: Dynamic Zone for Microbe- Driven 302
11.2.1.3 Microbe- to- Microbe Signaling 302
11.2.1.4 Plant- to- Microbe Signaling 303
11.2.1.5 Microbe- to- Plant Signaling 303
11.2.2 Functions in Growth Promotion and Immunity 303
11.3 AI Approaches in Microbiome Research 304
11.4 The Omics Toolkit: Mapping the Plant– Microbiome Interface 306
11.4.1 Metagenomics and the Functional Potential of Microbial Communities 307
11.4.2 Transcriptomics, Proteomics, and Metabolomics: Decoding Functional Expression 307
11.4.3 AI Pipelines for Feature Extraction and Data Harmonization 307
11.5 Predictive Applications 309
11.5.1 Biomarker Discovery: From Single Molecules to Functional Pathways 309
11.5.2 Disease Prediction and Early- Warning Systems (EWS) 310
11.6 Case Studies 312
11.7 Toward Predictive Microbiome Engineering 313
11.8 Challenges and Future Prospects 314
11.9 Conclusion 316
Acknowledgments 316
References 316
12 AI- Assisted Omics Tools for Predicting Functions of Plant RNAs 327
Ather Manzoor, Umer Fayaz, Nelofar Lone, Asma Majid, Showkat A. Waza, and A. K. M. Aminul Islam
12.1 Introduction 327
12.2 Omics Data in Plant RNA Studies 328
12.3 AI Methodologies in Omics Analysis 328
12.4 AI- Assisted Omics Tools for Predicting Plant RNA Functions 330
12.4.1 Plant Long Noncoding RNA Prediction by Random Forests9 (PLncPRO) 330
12.4.2 Plant Target Prediction for microRNAs (P- TarPmiR) 331
12.4.3 Plant Long Non- coding RNA Identification Tool (PLIT) 332
12.4.4 Plant RNA– FM (Plant RNA Foundation Model) 333
12.4.5 Plant Long Noncoding RNA– Protein Interaction Method (PLRPIM) 334
12.4.6 Abiotic Stress Long Non- coding RNA Predictor (ASLnCR) 335
12.4.7 Alternative Splicing and microRNA Interaction Resource (ASmiR) 335
12.4.8 miRNA Finder/MicroRNA Finder 336
12.5 Challenges 337
12.6 Conclusion 338
References 338
13 The Integration of Artificial Intelligence and Big Data in Plant Epigenetics 341
Isha Sharma, Varucha Misra, and A.K. Mall
13.1 Introduction 341
13.2 Fundamentals of Plant Epigenetics 343
13.2.1 Overview of Epigenetic Mechanisms 343
13.2.2 Function in Adaptation, Stress Response, and Plant Development 343
13.3 From Field to Cloud: Big Data Transforming Plant Research 344
13.4 AI Unraveled: Techniques for Biological Pattern Discovery 350
13.4.1 ml Approaches for Biological Data Analysis 350
13.4.2 dl Versus ml in Biological Data Analysis 351
13.5 Integrative Approaches Using AI and Big Data 354
13.5.1 AI- driven Identification of Epigenetic Biomarkers for Breeding 354
13.5.2 Network- Based Models for Epigenetic Regulation 355
13.5.3 Integrating Epigenomic, Transcriptomic, and Phenotypic Data 355
13.5.4 Case Study: AI- Based Identification of Rice Epigenetic Markers Responsive to Drought 357
13.5.5 Case Study: DL for Arabidopsis Chromatin Accessibility Prediction 358
13.6 From Code to Crops: AI Applications in Plant Epigenetics 358
13.6.1 Prediction of Histone Modification Sites and DM 358
13.6.2 Recognizing Regulatory Elements and Chromatin State 359
13.6.3 Categorizing Epigenetic Patterns Under Stress 359
13.6.4 Utilizing AI to Predict Genotypes to Epigenotypes 360
13.7 Future Perspectives 360
13.8 Conclusions 361
References 361
14 AI- Omics- Epigenetics Integration in Plants: Highlighting the Study of MicroRNAs 371
Aditya Pratap Singh, Sanjivani Karki, Ashutosh Sawarkar, and Siddhartha Singh
14.1 Introduction 371
14.2 Molecular Basis of Plant miRNA- Mediated Regulation 373
14.3 Epigenetic Roles of miRNAs in Plants 375
14.4 Artificial Intelligence in Biological Data Science 376
14.4.1 Relevance of AI to Plant miRNA Research 377
14.4.1.1 De Novo Discovery 377
14.4.2 Target Prediction and Interaction Modeling 377
14.4.3 Pattern Recognition in Multiomics 377
14.4.4 Translational Potential: From Basic Research to Precision Agriculture 377
14.5 AI and ML Techniques for MiRNA Discovery and Prediction 378
14.6 Computational Approaches in miRNA Discovery 379
14.7 ml Models for Plant miRNA Prediction 379
14.8 dl Applications and Target Prediction 380
14.8.1 Comparative Analysis of Computational Tools for Plant miRNA– Target Prediction 380
14.9 Conventional Prediction Tools and Their Limitations 380
14.10 dl Advancements 382
14.10.1 Convolutional Neural Networks 382
14.10.2 Graph Neural Networks 383
14.11 Integrating Multiomics and Epigenetic Data 383
14.11.1 Data Fusion Strategies 383
14.11.2 Epigenetic Features That Matter 384
14.11.3 Applications to Stress Biology 384
14.12 Challenges and Future Directions 384
14.12.1 AI- Driven Functional Analysis and Epigenetic Integration 385
14.12.2 Advancing Functional Analysis Through Artificial Intelligence 386
14.12.3 Decoding the Epigenetic Landscape with AI 386
14.12.4 Holistic Insights Through Multiomics Integration 387
14.12.5 Practical Applications in Modern Plant Science 388
14.12.6 Dynamic Modeling: Agent- Based and Reinforcement Learning 388
14.12.7 Ongoing Challenges and Future Trajectories 389
14.13 Conclusion 390
References 391
15 Machine Learning and Computational Biology- Based Epigenetics for Uncovering Plant Adaptive Evolution 397
Thiruvengadam Abarna, S. Gomathi, Shobana Devi Paulraj, Thirunethiran Karpagam, Angappan Shanmugapriya, and Ramasamy Manikandan
15.1 Introduction 397
15.1.1 Computational Challenges in Epigenetics 397
15.1.2 The Challenge of Phenotypic Plasticity and Rapid Adaptation 398
15.1.3 Epigenetics: A Bridge Between Genome and Environment 399
15.1.4 Navigating the Data Deluge 399
15.2 Foundational Concepts in Plant Epigenetics 399
15.2.1 Key Epigenetic Marks 399
15.2.1.1 DNA Methylation 400
15.2.1.2 Histone Modifications 401
15.2.1.3 Noncoding RNA- Associated Gene Silencing 401
15.2.2 Mechanisms of Epigenetic Inheritance: Mitotic and Meiotic 402
15.2.3 Epigenetic Regulation of Key Agronomic Traits 402
15.2.3.1 Epigenetic Basis of Flowering Time 402
15.2.3.2 Epigenetic Basis of Stress Memory 402
15.2.3.3 Epigenetic Regulation of Disease Resistance 403
15.3 Acquiring and Processing Epigenomic Data 404
15.3.1 High- Throughput Sequencing Technologies for Epigenomics 404
15.3.1.1 Bisulfite Sequencing (BS- seq, WGBS) for DNA Methylation 404
15.3.1.2 ChIP- seq for Histone Modifications 404
15.3.1.3 ATAC- seq for Chromatin Accessibility 404
15.3.2 Preprocessing and Quality Control of NGS Data 404
15.3.3 Core Bioinformatics Pipelines: Alignment, Peak Calling, and Differential Analysis 405
15.4 Machine Learning for Decoding the Epigenomic Language 405
15.4.1 Dimensionality Reduction and Pattern Discovery 405
15.4.1.1 Unsupervised Learning for Epigenome Exploration 406
15.4.2 Supervised Learning for Predictive Epigenomics 406
15.4.3 Deep Learning Architectures for Sequence and Function 407
15.4.3.1 Convolutional Neural Networks (CNNs) for cis- Regulatory Element Detection 407
15.4.3.2 Recurrent Neural Networks (RNNs/LSTMs) for Modeling Epigenomic Dynamics 408
15.5 Integrative Computational Biology for Evolutionary Insights 408
15.5.1 Multiomics Data Integration 408
15.5.2 Phylogenetic Comparative Methods for Epigenetics 409
15.5.3 Identifying Epigenetic Footprints of Selection and Domestication 409
15.6 Challenges and Future Directions 410
15.7 Conclusion 411
References 411
16 Ethical and Regulatory Considerations of Artificial Intelligence in Agriculture 417
Harshit Mishra, Fredrick Kayusi, Rashmi Mishra, Ioannis Adamopoulos, and Arkan A. Ghaib
16.1 Introduction 417
16.2 Ethical Foundations and Philosophical Underpinnings of AI in Agriculture 420
16.2.1 Ethical Theories and Frameworks Relevant to AI 420
16.2.1.1 Utilitarianism and Its Role in Agricultural Decision- Making 420
16.2.1.2 Deontological Ethics in Algorithmic Accountability 421
16.2.1.3 Virtue Ethics and Sustainable AI Development 421
16.2.2 Ethical Dilemmas Specific to Agricultural AI Systems 422
16.2.2.1 Data Ownership and Consent in Agricultural Datasets 422
16.2.2.2 Moral Implications of Replacing Human Labor 422
16.2.2.3 Issues of Equity and Fair Access to AI Tools 422
16.2.3 AI and the Ethical Impacts on Agroecosystems 423
16.2.3.1 Balancing Technological Advancement with Biodiversity 423
16.2.3.2 Ethical Trade- offs in Resource Allocation and Sustainability 423
16.3 Data Privacy, Security, and Intellectual Property Rights 424
16.3.1 Data Collection and Privacy in Agricultural Settings 424
16.3.1.1 Informed Consent and Farmer Autonomy 425
16.3.1.2 Anonymization and Aggregation of Agricultural Data 425
16.3.2 Data Governance and Security Protocols 425
16.3.2.1 Cybersecurity Risks in AI- Enabled Agricultural Systems 426
16.3.2.2 Blockchain and Distributed Ledger Technologies for Data Integrity 426
16.3.3 Intellectual Property and Proprietary Algorithms 427
16.3.3.1 Ownership Rights over AI- Generated Outputs 427
16.3.3.2 Patentability of AI Tools in Precision Agriculture 428
16.3.3.3 Licensing and Open- Source Frameworks 428
16.4 Bias, Fairness, and Transparency in Agricultural AI Models 429
16.4.1 Algorithmic Bias in Agronomic Predictions 430
16.4.1.1 Sources of Bias in Agricultural Datasets 430
16.4.1.2 Impacts on Marginalized Farming Communities 430
16.4.2 Fairness in Decision- Making and Resource Allocation 431
16.4.2.1 Equity in Yield Prediction and Crop Recommendation Systems 431
16.4.2.2 Inclusion of Smallholder Farmers in AI Model Training 431
16.4.3 Model Explainability and Transparency 432
16.4.3.1 Interpretable AI Approaches in Plant Epigenetics 432
16.4.3.2 Auditable AI Systems and Ethical Benchmarks 432
16.5 Legal and Regulatory Frameworks Governing AI in Agriculture 433
16.5.1 Overview of Existing Legal Structures 433
16.5.1.1 International AI Ethics Guidelines and Declarations 434
16.5.1.2 National Policies on Digital Agriculture and AI 434
16.5.2 Sector- Specific Regulations and Compliance 435
16.5.2.1 Regulatory Frameworks for Precision Agriculture Tools 435
16.5.2.2 Compliance with Environmental and Biotechnological Laws 435
16.5.3 Need for Dynamic and Adaptive Regulatory Mechanisms 436
16.5.3.1 Challenges in Regulating Evolving AI Technologies 436
16.5.3.2 Stakeholder Participation in Policy Formation 436
16.6 Ethical Considerations in Automated Decision- Making Systems 437
16.6.1 Autonomy and Control in AI- Driven Agricultural Decisions 437
16.6.1.1 Human- in- the- Loop Versus Fully Automated Systems 437
16.6.1.2 Accountability in Autonomous Agricultural Machinery 438
16.6.2 Risk Assessment and Unintended Consequences 438
16.6.2.1 Systemic Risks in Crop and Soil Management Algorithms 438
16.6.2.2 Ethical Issues in Predictive Failure and False Recommendations 439
16.6.3 Redress and Liability Mechanisms 439
16.6.3.1 Legal Responsibility for AI- Induced Harm 439
16.6.3.2 Dispute Resolution and Farmer Rights 440
16.7 Governance, Ethics Integration, and Future Directions 440
16.7.1 Institutional Frameworks for Ethical Oversight 440
16.7.1.1 Ethical Review Boards for Agricultural AI Projects 441
16.7.1.2 Cross- disciplinary Ethical Committees 441
16.7.2 Ethics- by- Design and Responsible AI Development 442
16.7.2.1 Embedding Ethical Principles in AI System Design 442
16.7.2.2 Participatory Design Approaches in Agricultural Technology 442
16.7.3 Global Cooperation and Ethical Standardization 443
16.7.3.1 Harmonization of Global Ethical Standards 443
16.7.3.2 Role of International Bodies and Consortia 443
16.8 Conclusion 444
References 444
Index 451
